arXiv:2608. 02985v1 Announce Type: new Abstract: The standard check for contamination in LLM backtests is simple: compare scores before and after the training cutoff.
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arXiv:2606. 11267v1 Announce Type: new Abstract: Data leakage -- contamination of a model with information unavailable at baseline -- is the dominant reproducibility failure in machine-learning-based science, yet detection tools require training code, external data, or domain expertise.
The paper introduces a 96‑item paired benchmark for evaluating large language models (LLMs) on backtest auditing, where each flawed backtest is matched with a clean control that keeps strategy, dates, code style, labels, and reporting scaffold constant while altering a single methodological detail. A deterministic scorer distinguishes flaw recall, clean‑control false positives, evidence localization, and fix relevance. Experiments on 1440 cached audits from four text endpoints show that the DeepSeek auditor achieves perfect closed and clean‑aware code recall, yet open prompts over‑flag 93.8% of clean controls, and clean‑aware specificity is 87.5% even when recall saturates. Introducing a clean‑aware warning eliminates 20.8% false positives to 0% without affecting recall, though the budget anchor still flags many clean controls. Reporting clean‑control rates provides a clearer differentiation among models than reporting recall alone.
Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item paired benchmark in which every flawed backtest has...
arXiv:2608.21606v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating whet...
arXiv:2608. 16795v1 Announce Type: cross Abstract: Systems that generate scientific research questions are evaluated today by expert scores, LLM-as-judge ratings, or curated case studies -- all subjective, none falsifiable.
The paper introduces a new taxonomy for benchmark contamination that categorizes leakage by the mitigation it defeats—direct, derivative, temporal, distributional, and acquired—covering both training‑time and evaluation‑time scenarios. It proposes a four‑field disclosure protocol to record contamination status alongside benchmark scores, and provides a JSON schema, validator, and examples. An empirical study of 41 documents using a pre‑registered instrument shows limited reporting of contamination types and variable reliability, highlighting gaps in current disclosure practices.